Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations7043
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 MiB
Average record size in memory738.1 B

Variable types

Categorical8
Boolean16
Numeric15
Text1

Alerts

Age is highly overall correlated with Senior_Citizen and 1 other fieldsHigh correlation
Avg_Monthly_GB_Download is highly overall correlated with Internet_Service and 2 other fieldsHigh correlation
Avg_Monthly_Long_Distance_Charges is highly overall correlated with Phone_Service and 1 other fieldsHigh correlation
Churn_Category is highly overall correlated with Churn_Label and 1 other fieldsHigh correlation
Churn_Label is highly overall correlated with Churn_Category and 3 other fieldsHigh correlation
Churn_Reason is highly overall correlated with Churn_Category and 1 other fieldsHigh correlation
Churn_Score is highly overall correlated with Churn_LabelHigh correlation
Device_Protection_Plan is highly overall correlated with Monthly_Charge and 1 other fieldsHigh correlation
Internet_Service is highly overall correlated with Avg_Monthly_GB_Download and 2 other fieldsHigh correlation
Internet_Type is highly overall correlated with Monthly_ChargeHigh correlation
Married is highly overall correlated with Number_of_ReferralsHigh correlation
Monthly_Charge is highly overall correlated with Device_Protection_Plan and 10 other fieldsHigh correlation
Multiple_Lines is highly overall correlated with Monthly_ChargeHigh correlation
Number_of_Referrals is highly overall correlated with MarriedHigh correlation
Offer is highly overall correlated with Tenure_in_MonthsHigh correlation
Online_Backup is highly overall correlated with Total_ChargesHigh correlation
Phone_Service is highly overall correlated with Avg_Monthly_Long_Distance_Charges and 1 other fieldsHigh correlation
Satisfaction_Score is highly overall correlated with Churn_LabelHigh correlation
Senior_Citizen is highly overall correlated with AgeHigh correlation
Streaming_Movies is highly overall correlated with Monthly_Charge and 3 other fieldsHigh correlation
Streaming_Music is highly overall correlated with Monthly_Charge and 1 other fieldsHigh correlation
Streaming_TV is highly overall correlated with Monthly_Charge and 2 other fieldsHigh correlation
Tenure_in_Months is highly overall correlated with Offer and 3 other fieldsHigh correlation
Total_Charges is highly overall correlated with Device_Protection_Plan and 7 other fieldsHigh correlation
Total_Long_Distance_Charges is highly overall correlated with Avg_Monthly_Long_Distance_Charges and 3 other fieldsHigh correlation
Total_Revenue is highly overall correlated with Monthly_Charge and 3 other fieldsHigh correlation
Under_30 is highly overall correlated with Age and 1 other fieldsHigh correlation
Unlimited_Data is highly overall correlated with Avg_Monthly_GB_Download and 2 other fieldsHigh correlation
Churn_Reason is highly imbalanced (58.0%) Imbalance
Phone_Service is highly imbalanced (54.1%) Imbalance
Number_of_Referrals has 3821 (54.3%) zeros Zeros
Avg_Monthly_Long_Distance_Charges has 682 (9.7%) zeros Zeros
Avg_Monthly_GB_Download has 1526 (21.7%) zeros Zeros
Total_Refunds has 6518 (92.5%) zeros Zeros
Total_Extra_Data_Charges has 6315 (89.7%) zeros Zeros
Total_Long_Distance_Charges has 682 (9.7%) zeros Zeros
Number_of_Dependents has 5416 (76.9%) zeros Zeros

Reproduction

Analysis started2024-10-18 19:36:29.729800
Analysis finished2024-10-18 19:37:08.759715
Duration39.03 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

Satisfaction_Score
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size399.0 KiB
3
2665 
4
1789 
5
1149 
1
922 
2
518 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 2665
37.8%
4 1789
25.4%
5 1149
16.3%
1 922
 
13.1%
2 518
 
7.4%

Length

2024-10-18T19:37:08.868285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:09.601555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2665
37.8%
4 1789
25.4%
5 1149
16.3%
1 922
 
13.1%
2 518
 
7.4%

Most occurring characters

ValueCountFrequency (%)
3 2665
37.8%
4 1789
25.4%
5 1149
16.3%
1 922
 
13.1%
2 518
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2665
37.8%
4 1789
25.4%
5 1149
16.3%
1 922
 
13.1%
2 518
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2665
37.8%
4 1789
25.4%
5 1149
16.3%
1 922
 
13.1%
2 518
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2665
37.8%
4 1789
25.4%
5 1149
16.3%
1 922
 
13.1%
2 518
 
7.4%

Churn_Label
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5174 
True
1869 
ValueCountFrequency (%)
False 5174
73.5%
True 1869
 
26.5%
2024-10-18T19:37:09.745433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Churn_Score
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.50504
Minimum5
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:09.881752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile24
Q140
median61
Q375.5
95-th percentile91
Maximum96
Range91
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation21.170031
Coefficient of variation (CV)0.36184969
Kurtosis-1.08529
Mean58.50504
Median Absolute Deviation (MAD)17
Skewness-0.15685222
Sum412051
Variance448.1702
MonotonicityNot monotonic
2024-10-18T19:37:10.047104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 202
 
2.9%
80 150
 
2.1%
71 148
 
2.1%
77 145
 
2.1%
67 142
 
2.0%
68 140
 
2.0%
76 140
 
2.0%
90 139
 
2.0%
70 138
 
2.0%
69 138
 
2.0%
Other values (71) 5561
79.0%
ValueCountFrequency (%)
5 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 3
 
< 0.1%
20 83
1.2%
21 84
1.2%
22 82
1.2%
23 78
1.1%
24 86
1.2%
25 85
1.2%
ValueCountFrequency (%)
96 49
 
0.7%
95 46
 
0.7%
94 46
 
0.7%
93 50
 
0.7%
92 51
 
0.7%
91 202
2.9%
90 139
2.0%
89 60
 
0.9%
88 33
 
0.5%
87 65
 
0.9%

CLTV
Real number (ℝ)

Distinct3438
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.2958
Minimum2003
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:10.218476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2296
Q13469
median4527
Q35380.5
95-th percentile6087
Maximum6500
Range4497
Interquartile range (IQR)1911.5

Descriptive statistics

Standard deviation1183.0572
Coefficient of variation (CV)0.26885855
Kurtosis-0.93403248
Mean4400.2958
Median Absolute Deviation (MAD)922
Skewness-0.3116021
Sum30991283
Variance1399624.2
MonotonicityNot monotonic
2024-10-18T19:37:10.377619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5546 8
 
0.1%
4741 7
 
0.1%
4745 7
 
0.1%
4369 7
 
0.1%
4115 7
 
0.1%
5915 7
 
0.1%
5527 7
 
0.1%
5461 7
 
0.1%
5137 7
 
0.1%
5092 7
 
0.1%
Other values (3428) 6972
99.0%
ValueCountFrequency (%)
2003 3
< 0.1%
2004 3
< 0.1%
2006 1
 
< 0.1%
2007 4
0.1%
2008 1
 
< 0.1%
2009 2
< 0.1%
2010 3
< 0.1%
2011 2
< 0.1%
2013 2
< 0.1%
2014 1
 
< 0.1%
ValueCountFrequency (%)
6500 1
 
< 0.1%
6499 2
< 0.1%
6495 1
 
< 0.1%
6494 2
< 0.1%
6492 3
< 0.1%
6491 1
 
< 0.1%
6490 1
 
< 0.1%
6489 1
 
< 0.1%
6488 1
 
< 0.1%
6487 2
< 0.1%

Churn_Category
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size464.9 KiB
Not Churned
5174 
Competitor
841 
Attitude
 
314
Dissatisfaction
 
303
Price
 
211

Length

Max length15
Median length11
Mean length10.568792
Min length5

Characters and Unicode

Total characters74436
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Churned
2nd rowNot Churned
3rd rowCompetitor
4th rowDissatisfaction
5th rowDissatisfaction

Common Values

ValueCountFrequency (%)
Not Churned 5174
73.5%
Competitor 841
 
11.9%
Attitude 314
 
4.5%
Dissatisfaction 303
 
4.3%
Price 211
 
3.0%
Other 200
 
2.8%

Length

2024-10-18T19:37:10.518250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:10.665550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
not 5174
42.4%
churned 5174
42.4%
competitor 841
 
6.9%
attitude 314
 
2.6%
dissatisfaction 303
 
2.5%
price 211
 
1.7%
other 200
 
1.6%

Most occurring characters

ValueCountFrequency (%)
t 8604
11.6%
o 7159
9.6%
e 6740
9.1%
r 6426
8.6%
C 6015
8.1%
u 5488
7.4%
d 5488
7.4%
n 5477
7.4%
h 5374
7.2%
N 5174
7.0%
Other values (12) 12491
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 8604
11.6%
o 7159
9.6%
e 6740
9.1%
r 6426
8.6%
C 6015
8.1%
u 5488
7.4%
d 5488
7.4%
n 5477
7.4%
h 5374
7.2%
N 5174
7.0%
Other values (12) 12491
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 8604
11.6%
o 7159
9.6%
e 6740
9.1%
r 6426
8.6%
C 6015
8.1%
u 5488
7.4%
d 5488
7.4%
n 5477
7.4%
h 5374
7.2%
N 5174
7.0%
Other values (12) 12491
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 8604
11.6%
o 7159
9.6%
e 6740
9.1%
r 6426
8.6%
C 6015
8.1%
u 5488
7.4%
d 5488
7.4%
n 5477
7.4%
h 5374
7.2%
N 5174
7.0%
Other values (12) 12491
16.8%

Churn_Reason
Categorical

High correlation  Imbalance 

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size493.8 KiB
Not Churned
5174 
Competitor had better devices
 
313
Competitor made better offer
 
311
Attitude of support person
 
220
Don't know
 
130
Other values (16)
895 

Length

Max length41
Median length11
Mean length14.783331
Min length5

Characters and Unicode

Total characters104119
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Churned
2nd rowNot Churned
3rd rowCompetitor had better devices
4th rowProduct dissatisfaction
5th rowNetwork reliability

Common Values

ValueCountFrequency (%)
Not Churned 5174
73.5%
Competitor had better devices 313
 
4.4%
Competitor made better offer 311
 
4.4%
Attitude of support person 220
 
3.1%
Don't know 130
 
1.8%
Competitor offered more data 117
 
1.7%
Competitor offered higher download speeds 100
 
1.4%
Attitude of service provider 94
 
1.3%
Price too high 78
 
1.1%
Product dissatisfaction 77
 
1.1%
Other values (11) 429
 
6.1%

Length

2024-10-18T19:37:10.819946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
not 5174
30.4%
churned 5174
30.4%
competitor 841
 
4.9%
better 624
 
3.7%
of 453
 
2.7%
attitude 314
 
1.8%
had 313
 
1.8%
devices 313
 
1.8%
made 311
 
1.8%
offer 311
 
1.8%
Other values (39) 3177
18.7%

Most occurring characters

ValueCountFrequency (%)
e 11312
10.9%
t 10386
10.0%
9962
9.6%
o 9824
9.4%
r 8872
8.5%
d 7712
 
7.4%
n 6192
 
5.9%
C 6015
 
5.8%
h 5958
 
5.7%
u 5858
 
5.6%
Other values (27) 22028
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11312
10.9%
t 10386
10.0%
9962
9.6%
o 9824
9.4%
r 8872
8.5%
d 7712
 
7.4%
n 6192
 
5.9%
C 6015
 
5.8%
h 5958
 
5.7%
u 5858
 
5.6%
Other values (27) 22028
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11312
10.9%
t 10386
10.0%
9962
9.6%
o 9824
9.4%
r 8872
8.5%
d 7712
 
7.4%
n 6192
 
5.9%
C 6015
 
5.8%
h 5958
 
5.7%
u 5858
 
5.6%
Other values (27) 22028
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11312
10.9%
t 10386
10.0%
9962
9.6%
o 9824
9.4%
r 8872
8.5%
d 7712
 
7.4%
n 6192
 
5.9%
C 6015
 
5.8%
h 5958
 
5.7%
u 5858
 
5.6%
Other values (27) 22028
21.2%

Number_of_Referrals
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9518671
Minimum0
Maximum11
Zeros3821
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:10.956481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0011993
Coefficient of variation (CV)1.5376043
Kurtosis0.72196393
Mean1.9518671
Median Absolute Deviation (MAD)0
Skewness1.4460596
Sum13747
Variance9.0071972
MonotonicityNot monotonic
2024-10-18T19:37:11.068975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
5 264
 
3.7%
3 255
 
3.6%
7 248
 
3.5%
9 238
 
3.4%
2 236
 
3.4%
4 236
 
3.4%
10 223
 
3.2%
6 221
 
3.1%
Other values (2) 215
 
3.1%
ValueCountFrequency (%)
0 3821
54.3%
1 1086
 
15.4%
2 236
 
3.4%
3 255
 
3.6%
4 236
 
3.4%
5 264
 
3.7%
6 221
 
3.1%
7 248
 
3.5%
8 213
 
3.0%
9 238
 
3.4%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 223
3.2%
9 238
3.4%
8 213
3.0%
7 248
3.5%
6 221
3.1%
5 264
3.7%
4 236
3.4%
3 255
3.6%
2 236
3.4%

Tenure_in_Months
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.386767
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:11.220559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.542061
Coefficient of variation (CV)0.75778052
Kurtosis-1.3870524
Mean32.386767
Median Absolute Deviation (MAD)22
Skewness0.24054261
Sum228100
Variance602.31276
MonotonicityNot monotonic
2024-10-18T19:37:11.385671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 613
 
8.7%
72 362
 
5.1%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
71 170
 
2.4%
5 133
 
1.9%
7 131
 
1.9%
10 127
 
1.8%
8 123
 
1.7%
Other values (62) 4770
67.7%
ValueCountFrequency (%)
1 613
8.7%
2 238
 
3.4%
3 200
 
2.8%
4 176
 
2.5%
5 133
 
1.9%
6 110
 
1.6%
7 131
 
1.9%
8 123
 
1.7%
9 119
 
1.7%
10 127
 
1.8%
ValueCountFrequency (%)
72 362
5.1%
71 170
2.4%
70 119
 
1.7%
69 95
 
1.3%
68 100
 
1.4%
67 98
 
1.4%
66 89
 
1.3%
65 76
 
1.1%
64 80
 
1.1%
63 72
 
1.0%

Offer
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size444.1 KiB
No Offer
3877 
Offer B
824 
Offer E
805 
Offer D
602 
Offer A
520 

Length

Max length8
Median length8
Mean length7.5504756
Min length7

Characters and Unicode

Total characters53178
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Offer
2nd rowNo Offer
3rd rowOffer E
4th rowOffer D
5th rowNo Offer

Common Values

ValueCountFrequency (%)
No Offer 3877
55.0%
Offer B 824
 
11.7%
Offer E 805
 
11.4%
Offer D 602
 
8.5%
Offer A 520
 
7.4%
Offer C 415
 
5.9%

Length

2024-10-18T19:37:11.539895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:11.692275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
offer 7043
50.0%
no 3877
27.5%
b 824
 
5.8%
e 805
 
5.7%
d 602
 
4.3%
a 520
 
3.7%
c 415
 
2.9%

Most occurring characters

ValueCountFrequency (%)
f 14086
26.5%
7043
13.2%
O 7043
13.2%
e 7043
13.2%
r 7043
13.2%
N 3877
 
7.3%
o 3877
 
7.3%
B 824
 
1.5%
E 805
 
1.5%
D 602
 
1.1%
Other values (2) 935
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 14086
26.5%
7043
13.2%
O 7043
13.2%
e 7043
13.2%
r 7043
13.2%
N 3877
 
7.3%
o 3877
 
7.3%
B 824
 
1.5%
E 805
 
1.5%
D 602
 
1.1%
Other values (2) 935
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 14086
26.5%
7043
13.2%
O 7043
13.2%
e 7043
13.2%
r 7043
13.2%
N 3877
 
7.3%
o 3877
 
7.3%
B 824
 
1.5%
E 805
 
1.5%
D 602
 
1.1%
Other values (2) 935
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 14086
26.5%
7043
13.2%
O 7043
13.2%
e 7043
13.2%
r 7043
13.2%
N 3877
 
7.3%
o 3877
 
7.3%
B 824
 
1.5%
E 805
 
1.5%
D 602
 
1.1%
Other values (2) 935
 
1.8%

Phone_Service
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True 6361
90.3%
False 682
 
9.7%
2024-10-18T19:37:11.844228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Avg_Monthly_Long_Distance_Charges
Real number (ℝ)

High correlation  Zeros 

Distinct3584
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.958954
Minimum0
Maximum49.99
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:11.978465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.21
median22.89
Q336.395
95-th percentile47.34
Maximum49.99
Range49.99
Interquartile range (IQR)27.185

Descriptive statistics

Standard deviation15.448113
Coefficient of variation (CV)0.6728579
Kurtosis-1.2546544
Mean22.958954
Median Absolute Deviation (MAD)13.6
Skewness0.049175899
Sum161699.91
Variance238.64421
MonotonicityNot monotonic
2024-10-18T19:37:12.155218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
18.26 7
 
0.1%
30.07 6
 
0.1%
45.92 6
 
0.1%
30.09 6
 
0.1%
42.55 6
 
0.1%
49.51 6
 
0.1%
22.56 6
 
0.1%
22.83 6
 
0.1%
41.93 6
 
0.1%
Other values (3574) 6306
89.5%
ValueCountFrequency (%)
0 682
9.7%
1.01 1
 
< 0.1%
1.02 3
 
< 0.1%
1.03 1
 
< 0.1%
1.05 1
 
< 0.1%
1.06 1
 
< 0.1%
1.07 1
 
< 0.1%
1.08 2
 
< 0.1%
1.09 2
 
< 0.1%
1.1 1
 
< 0.1%
ValueCountFrequency (%)
49.99 1
 
< 0.1%
49.98 3
< 0.1%
49.96 2
< 0.1%
49.95 2
< 0.1%
49.94 1
 
< 0.1%
49.92 1
 
< 0.1%
49.91 3
< 0.1%
49.9 3
< 0.1%
49.88 1
 
< 0.1%
49.87 1
 
< 0.1%

Multiple_Lines
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4072 
True
2971 
ValueCountFrequency (%)
False 4072
57.8%
True 2971
42.2%
2024-10-18T19:37:12.316200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Internet_Service
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
5517 
False
1526 
ValueCountFrequency (%)
True 5517
78.3%
False 1526
 
21.7%
2024-10-18T19:37:12.439490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Internet_Type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.1 KiB
Fiber Optic
4561 
DSL
1652 
Cable
830 

Length

Max length11
Median length11
Mean length8.4164419
Min length3

Characters and Unicode

Total characters59277
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCable
2nd rowCable
3rd rowFiber Optic
4th rowFiber Optic
5th rowFiber Optic

Common Values

ValueCountFrequency (%)
Fiber Optic 4561
64.8%
DSL 1652
 
23.5%
Cable 830
 
11.8%

Length

2024-10-18T19:37:12.562213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:12.712025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
fiber 4561
39.3%
optic 4561
39.3%
dsl 1652
 
14.2%
cable 830
 
7.2%

Most occurring characters

ValueCountFrequency (%)
i 9122
15.4%
b 5391
9.1%
e 5391
9.1%
F 4561
7.7%
r 4561
7.7%
4561
7.7%
O 4561
7.7%
p 4561
7.7%
t 4561
7.7%
c 4561
7.7%
Other values (6) 7446
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 9122
15.4%
b 5391
9.1%
e 5391
9.1%
F 4561
7.7%
r 4561
7.7%
4561
7.7%
O 4561
7.7%
p 4561
7.7%
t 4561
7.7%
c 4561
7.7%
Other values (6) 7446
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 9122
15.4%
b 5391
9.1%
e 5391
9.1%
F 4561
7.7%
r 4561
7.7%
4561
7.7%
O 4561
7.7%
p 4561
7.7%
t 4561
7.7%
c 4561
7.7%
Other values (6) 7446
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 9122
15.4%
b 5391
9.1%
e 5391
9.1%
F 4561
7.7%
r 4561
7.7%
4561
7.7%
O 4561
7.7%
p 4561
7.7%
t 4561
7.7%
c 4561
7.7%
Other values (6) 7446
12.6%

Avg_Monthly_GB_Download
Real number (ℝ)

High correlation  Zeros 

Distinct50
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.515405
Minimum0
Maximum85
Zeros1526
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:12.861589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median17
Q327
95-th percentile69
Maximum85
Range85
Interquartile range (IQR)24

Descriptive statistics

Standard deviation20.41894
Coefficient of variation (CV)0.99529792
Kurtosis0.88150231
Mean20.515405
Median Absolute Deviation (MAD)12
Skewness1.2165839
Sum144490
Variance416.93313
MonotonicityNot monotonic
2024-10-18T19:37:13.024657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1526
 
21.7%
19 220
 
3.1%
27 199
 
2.8%
30 193
 
2.7%
59 192
 
2.7%
26 191
 
2.7%
23 179
 
2.5%
22 172
 
2.4%
21 171
 
2.4%
18 164
 
2.3%
Other values (40) 3836
54.5%
ValueCountFrequency (%)
0 1526
21.7%
2 116
 
1.6%
3 130
 
1.8%
4 129
 
1.8%
5 114
 
1.6%
6 114
 
1.6%
7 116
 
1.6%
8 120
 
1.7%
9 116
 
1.6%
10 132
 
1.9%
ValueCountFrequency (%)
85 48
 
0.7%
82 43
 
0.6%
76 58
 
0.8%
75 15
 
0.2%
73 81
1.2%
71 42
 
0.6%
69 75
 
1.1%
59 192
2.7%
58 45
 
0.6%
57 34
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5024 
True
2019 
ValueCountFrequency (%)
False 5024
71.3%
True 2019
28.7%
2024-10-18T19:37:13.190744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Online_Backup
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4614 
True
2429 
ValueCountFrequency (%)
False 4614
65.5%
True 2429
34.5%
2024-10-18T19:37:13.325723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Device_Protection_Plan
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4621 
True
2422 
ValueCountFrequency (%)
False 4621
65.6%
True 2422
34.4%
2024-10-18T19:37:13.448056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4999 
True
2044 
ValueCountFrequency (%)
False 4999
71.0%
True 2044
29.0%
2024-10-18T19:37:13.571985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Streaming_TV
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4336 
True
2707 
ValueCountFrequency (%)
False 4336
61.6%
True 2707
38.4%
2024-10-18T19:37:13.696502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Streaming_Movies
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4311 
True
2732 
ValueCountFrequency (%)
False 4311
61.2%
True 2732
38.8%
2024-10-18T19:37:13.822364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Streaming_Music
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
4555 
True
2488 
ValueCountFrequency (%)
False 4555
64.7%
True 2488
35.3%
2024-10-18T19:37:13.946739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Unlimited_Data
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4745 
False
2298 
ValueCountFrequency (%)
True 4745
67.4%
False 2298
32.6%
2024-10-18T19:37:14.071041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Contract
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size468.3 KiB
Month-to-Month
3610 
Two Year
1883 
One Year
1550 

Length

Max length14
Median length14
Mean length11.075394
Min length8

Characters and Unicode

Total characters78004
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne Year
2nd rowMonth-to-Month
3rd rowMonth-to-Month
4th rowMonth-to-Month
5th rowMonth-to-Month

Common Values

ValueCountFrequency (%)
Month-to-Month 3610
51.3%
Two Year 1883
26.7%
One Year 1550
22.0%

Length

2024-10-18T19:37:14.189987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:14.339542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month 3610
34.5%
year 3433
32.8%
two 1883
18.0%
one 1550
14.8%

Most occurring characters

ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 12713
16.3%
t 10830
13.9%
n 8770
11.2%
M 7220
9.3%
h 7220
9.3%
- 7220
9.3%
e 4983
 
6.4%
3433
 
4.4%
Y 3433
 
4.4%
a 3433
 
4.4%
Other values (4) 8749
11.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True 4171
59.2%
False 2872
40.8%
2024-10-18T19:37:14.475196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Payment_Method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size483.5 KiB
Bank Withdrawal
3909 
Credit Card
2749 
Mailed Check
 
385

Length

Max length15
Median length15
Mean length13.274741
Min length11

Characters and Unicode

Total characters93494
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowBank Withdrawal
4th rowBank Withdrawal
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Bank Withdrawal 3909
55.5%
Credit Card 2749
39.0%
Mailed Check 385
 
5.5%

Length

2024-10-18T19:37:14.597933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:14.749731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
bank 3909
27.8%
withdrawal 3909
27.8%
credit 2749
19.5%
card 2749
19.5%
mailed 385
 
2.7%
check 385
 
2.7%

Most occurring characters

ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14861
15.9%
d 9792
10.5%
r 9407
10.1%
7043
 
7.5%
i 7043
 
7.5%
t 6658
 
7.1%
C 5883
 
6.3%
h 4294
 
4.6%
k 4294
 
4.6%
l 4294
 
4.6%
Other values (7) 19925
21.3%

Monthly_Charge
Real number (ℝ)

High correlation 

Distinct1585
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.761692
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:14.897057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.35
Q389.85
95-th percentile107.4
Maximum118.75
Range100.5
Interquartile range (IQR)54.35

Descriptive statistics

Standard deviation30.090047
Coefficient of variation (CV)0.46462725
Kurtosis-1.2572597
Mean64.761692
Median Absolute Deviation (MAD)24.05
Skewness-0.22052443
Sum456116.6
Variance905.41093
MonotonicityNot monotonic
2024-10-18T19:37:15.071243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.05 61
 
0.9%
19.85 45
 
0.6%
19.95 44
 
0.6%
19.9 44
 
0.6%
20 43
 
0.6%
19.65 43
 
0.6%
19.7 43
 
0.6%
19.55 40
 
0.6%
20.15 40
 
0.6%
20.25 39
 
0.6%
Other values (1575) 6601
93.7%
ValueCountFrequency (%)
18.25 1
 
< 0.1%
18.4 1
 
< 0.1%
18.55 1
 
< 0.1%
18.7 2
 
< 0.1%
18.75 1
 
< 0.1%
18.8 7
0.1%
18.85 5
0.1%
18.9 2
 
< 0.1%
18.95 6
0.1%
19 7
0.1%
ValueCountFrequency (%)
118.75 1
< 0.1%
118.65 1
< 0.1%
118.6 2
< 0.1%
118.35 1
< 0.1%
118.2 1
< 0.1%
117.8 1
< 0.1%
117.6 1
< 0.1%
117.5 1
< 0.1%
117.45 1
< 0.1%
117.35 1
< 0.1%

Total_Charges
Real number (ℝ)

High correlation 

Distinct6540
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2280.3813
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:15.246308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.65
Q1400.15
median1394.55
Q33786.6
95-th percentile6921.025
Maximum8684.8
Range8666
Interquartile range (IQR)3386.45

Descriptive statistics

Standard deviation2266.2205
Coefficient of variation (CV)0.99379016
Kurtosis-0.22769266
Mean2280.3813
Median Absolute Deviation (MAD)1219.75
Skewness0.96379109
Sum16060725
Variance5135755.2
MonotonicityNot monotonic
2024-10-18T19:37:15.410064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.2 11
 
0.2%
19.75 9
 
0.1%
19.9 8
 
0.1%
20.05 8
 
0.1%
19.65 8
 
0.1%
19.55 7
 
0.1%
45.3 7
 
0.1%
20.15 6
 
0.1%
19.45 6
 
0.1%
20.25 6
 
0.1%
Other values (6530) 6967
98.9%
ValueCountFrequency (%)
18.8 1
 
< 0.1%
18.85 2
< 0.1%
18.9 1
 
< 0.1%
19 1
 
< 0.1%
19.05 1
 
< 0.1%
19.1 3
< 0.1%
19.15 1
 
< 0.1%
19.2 4
0.1%
19.25 3
< 0.1%
19.3 4
0.1%
ValueCountFrequency (%)
8684.8 1
< 0.1%
8672.45 1
< 0.1%
8670.1 1
< 0.1%
8594.4 1
< 0.1%
8564.75 1
< 0.1%
8547.15 1
< 0.1%
8543.25 1
< 0.1%
8529.5 1
< 0.1%
8496.7 1
< 0.1%
8477.7 1
< 0.1%

Total_Refunds
Real number (ℝ)

Zeros 

Distinct500
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9621823
Minimum0
Maximum49.79
Zeros6518
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:15.573295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18.149
Maximum49.79
Range49.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9026144
Coefficient of variation (CV)4.0274618
Kurtosis18.350658
Mean1.9621823
Median Absolute Deviation (MAD)0
Skewness4.3285167
Sum13819.65
Variance62.451314
MonotonicityNot monotonic
2024-10-18T19:37:15.749059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6518
92.5%
16.56 2
 
< 0.1%
8.74 2
 
< 0.1%
1.31 2
 
< 0.1%
41.74 2
 
< 0.1%
25.67 2
 
< 0.1%
29.76 2
 
< 0.1%
18.55 2
 
< 0.1%
15.41 2
 
< 0.1%
27.6 2
 
< 0.1%
Other values (490) 507
 
7.2%
ValueCountFrequency (%)
0 6518
92.5%
1.01 1
 
< 0.1%
1.09 1
 
< 0.1%
1.27 1
 
< 0.1%
1.31 2
 
< 0.1%
1.48 1
 
< 0.1%
1.65 1
 
< 0.1%
1.66 1
 
< 0.1%
1.69 1
 
< 0.1%
1.83 1
 
< 0.1%
ValueCountFrequency (%)
49.79 1
< 0.1%
49.76 1
< 0.1%
49.57 2
< 0.1%
49.53 1
< 0.1%
49.51 1
< 0.1%
49.38 1
< 0.1%
49.37 1
< 0.1%
49.24 1
< 0.1%
49.23 1
< 0.1%
49.22 1
< 0.1%

Total_Extra_Data_Charges
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8607128
Minimum0
Maximum150
Zeros6315
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:15.892947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile60
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.104978
Coefficient of variation (CV)3.6592376
Kurtosis16.458874
Mean6.8607128
Median Absolute Deviation (MAD)0
Skewness4.0912092
Sum48320
Variance630.25992
MonotonicityNot monotonic
2024-10-18T19:37:16.013807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6315
89.7%
10 138
 
2.0%
40 62
 
0.9%
30 58
 
0.8%
20 51
 
0.7%
80 47
 
0.7%
100 44
 
0.6%
50 43
 
0.6%
150 42
 
0.6%
130 40
 
0.6%
Other values (6) 203
 
2.9%
ValueCountFrequency (%)
0 6315
89.7%
10 138
 
2.0%
20 51
 
0.7%
30 58
 
0.8%
40 62
 
0.9%
50 43
 
0.6%
60 36
 
0.5%
70 34
 
0.5%
80 47
 
0.7%
90 35
 
0.5%
ValueCountFrequency (%)
150 42
0.6%
140 38
0.5%
130 40
0.6%
120 28
0.4%
110 32
0.5%
100 44
0.6%
90 35
0.5%
80 47
0.7%
70 34
0.5%
60 36
0.5%

Total_Long_Distance_Charges
Real number (ℝ)

High correlation  Zeros 

Distinct6068
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean749.09926
Minimum0
Maximum3564.72
Zeros682
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:16.179404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170.545
median401.44
Q31191.1
95-th percentile2577.877
Maximum3564.72
Range3564.72
Interquartile range (IQR)1120.555

Descriptive statistics

Standard deviation846.66005
Coefficient of variation (CV)1.1302375
Kurtosis0.64409208
Mean749.09926
Median Absolute Deviation (MAD)382.12
Skewness1.238282
Sum5275906.1
Variance716833.25
MonotonicityNot monotonic
2024-10-18T19:37:16.349119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 682
 
9.7%
15.6 4
 
0.1%
48.96 4
 
0.1%
22.86 4
 
0.1%
597.6 3
 
< 0.1%
2077.92 3
 
< 0.1%
198 3
 
< 0.1%
15.28 3
 
< 0.1%
808.08 3
 
< 0.1%
41.1 3
 
< 0.1%
Other values (6058) 6331
89.9%
ValueCountFrequency (%)
0 682
9.7%
1.13 1
 
< 0.1%
1.15 1
 
< 0.1%
1.17 1
 
< 0.1%
1.23 1
 
< 0.1%
1.28 1
 
< 0.1%
1.47 1
 
< 0.1%
1.48 1
 
< 0.1%
1.5 1
 
< 0.1%
1.59 1
 
< 0.1%
ValueCountFrequency (%)
3564.72 1
< 0.1%
3564 1
< 0.1%
3536.64 1
< 0.1%
3515.92 1
< 0.1%
3508.82 1
< 0.1%
3501.72 1
< 0.1%
3493.44 1
< 0.1%
3492.72 1
< 0.1%
3487.68 1
< 0.1%
3482.64 1
< 0.1%

Total_Revenue
Real number (ℝ)

High correlation 

Distinct6975
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3034.3791
Minimum21.36
Maximum11979.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:16.522902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum21.36
5-th percentile78.452
Q1605.61
median2108.64
Q34801.145
95-th percentile8747.041
Maximum11979.34
Range11957.98
Interquartile range (IQR)4195.535

Descriptive statistics

Standard deviation2865.2045
Coefficient of variation (CV)0.9442474
Kurtosis-0.20345739
Mean3034.3791
Median Absolute Deviation (MAD)1767.61
Skewness0.91941027
Sum21371132
Variance8209397.1
MonotonicityNot monotonic
2024-10-18T19:37:16.698874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.8 3
 
< 0.1%
116.27 3
 
< 0.1%
68.41 3
 
< 0.1%
66.56 3
 
< 0.1%
3386.4 2
 
< 0.1%
174.93 2
 
< 0.1%
608.85 2
 
< 0.1%
712.85 2
 
< 0.1%
3423.5 2
 
< 0.1%
88.75 2
 
< 0.1%
Other values (6965) 7019
99.7%
ValueCountFrequency (%)
21.36 1
< 0.1%
21.4 1
< 0.1%
21.61 1
< 0.1%
22.08 1
< 0.1%
22.12 1
< 0.1%
22.25 1
< 0.1%
22.28 1
< 0.1%
22.54 1
< 0.1%
23.24 2
< 0.1%
23.28 1
< 0.1%
ValueCountFrequency (%)
11979.34 1
< 0.1%
11868.34 1
< 0.1%
11795.78 1
< 0.1%
11688.9 1
< 0.1%
11634.53 1
< 0.1%
11596.99 1
< 0.1%
11564.37 1
< 0.1%
11529.54 1
< 0.1%
11514.81 1
< 0.1%
11501.82 1
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size426.5 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 3555
50.5%
Female 3488
49.5%

Length

2024-10-18T19:37:16.859053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-18T19:37:17.014885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 3555
50.5%
female 3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10531
30.0%
a 7043
20.0%
l 7043
20.0%
M 3555
 
10.1%
F 3488
 
9.9%
m 3488
 
9.9%

Age
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.509726
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:17.152736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q132
median46
Q360
95-th percentile75
Maximum80
Range61
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.750352
Coefficient of variation (CV)0.36014729
Kurtosis-1.0028495
Mean46.509726
Median Absolute Deviation (MAD)14
Skewness0.16218645
Sum327568
Variance280.57428
MonotonicityNot monotonic
2024-10-18T19:37:17.317703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 156
 
2.2%
47 153
 
2.2%
40 150
 
2.1%
44 148
 
2.1%
23 146
 
2.1%
56 144
 
2.0%
62 143
 
2.0%
35 142
 
2.0%
21 140
 
2.0%
33 139
 
2.0%
Other values (52) 5582
79.3%
ValueCountFrequency (%)
19 127
1.8%
20 127
1.8%
21 140
2.0%
22 130
1.8%
23 146
2.1%
24 109
1.5%
25 138
2.0%
26 115
1.6%
27 132
1.9%
28 119
1.7%
ValueCountFrequency (%)
80 66
0.9%
79 76
1.1%
78 63
0.9%
77 72
1.0%
76 69
1.0%
75 74
1.1%
74 76
1.1%
73 85
1.2%
72 58
0.8%
71 68
1.0%

Under_30
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5642 
True
1401 
ValueCountFrequency (%)
False 5642
80.1%
True 1401
 
19.9%
2024-10-18T19:37:17.483030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Senior_Citizen
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5901 
True
1142 
ValueCountFrequency (%)
False 5901
83.8%
True 1142
 
16.2%
2024-10-18T19:37:17.609239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Married
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False 3641
51.7%
True 3402
48.3%
2024-10-18T19:37:17.735507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Number_of_Dependents
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46869232
Minimum0
Maximum9
Zeros5416
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:17.840819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96280195
Coefficient of variation (CV)2.0542303
Kurtosis4.4463579
Mean0.46869232
Median Absolute Deviation (MAD)0
Skewness2.109932
Sum3301
Variance0.9269876
MonotonicityNot monotonic
2024-10-18T19:37:17.948367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
5 10
 
0.1%
4 9
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 5416
76.9%
1 553
 
7.9%
2 531
 
7.5%
3 517
 
7.3%
4 9
 
0.1%
5 10
 
0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 3
 
< 0.1%
5 10
 
0.1%
4 9
 
0.1%
3 517
 
7.3%
2 531
 
7.5%
1 553
 
7.9%
0 5416
76.9%

City
Text

Distinct1106
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size455.5 KiB
2024-10-18T19:37:18.229989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length9.2034644
Min length3

Characters and Unicode

Total characters64820
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrazier Park
2nd rowGlendale
3rd rowCosta Mesa
4th rowMartinez
5th rowCamarillo
ValueCountFrequency (%)
san 718
 
6.9%
los 337
 
3.3%
angeles 293
 
2.8%
diego 285
 
2.8%
santa 181
 
1.8%
valley 171
 
1.7%
beach 169
 
1.6%
city 150
 
1.5%
sacramento 116
 
1.1%
jose 112
 
1.1%
Other values (1110) 7807
75.5%
2024-10-18T19:37:18.724422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6946
 
10.7%
e 6111
 
9.4%
n 5134
 
7.9%
o 5074
 
7.8%
l 3970
 
6.1%
r 3568
 
5.5%
i 3423
 
5.3%
3296
 
5.1%
s 2853
 
4.4%
t 2602
 
4.0%
Other values (42) 21843
33.7%

Zip_Code
Real number (ℝ)

Distinct1626
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93486.071
Minimum90001
Maximum96150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2024-10-18T19:37:18.924585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90241.1
Q192101
median93518
Q395329
95-th percentile96020.9
Maximum96150
Range6149
Interquartile range (IQR)3228

Descriptive statistics

Standard deviation1856.7675
Coefficient of variation (CV)0.019861435
Kurtosis-1.1739154
Mean93486.071
Median Absolute Deviation (MAD)1605
Skewness-0.20961512
Sum6.584224 × 108
Variance3447585.6
MonotonicityNot monotonic
2024-10-18T19:37:19.092655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92028 43
 
0.6%
92027 38
 
0.5%
92122 36
 
0.5%
92117 34
 
0.5%
92126 32
 
0.5%
92592 30
 
0.4%
92109 27
 
0.4%
92130 22
 
0.3%
92121 20
 
0.3%
92129 16
 
0.2%
Other values (1616) 6745
95.8%
ValueCountFrequency (%)
90001 4
0.1%
90002 4
0.1%
90003 5
0.1%
90004 5
0.1%
90005 4
0.1%
90006 5
0.1%
90007 5
0.1%
90008 5
0.1%
90010 4
0.1%
90011 5
0.1%
ValueCountFrequency (%)
96150 2
< 0.1%
96148 4
0.1%
96146 4
0.1%
96145 3
< 0.1%
96143 4
0.1%
96142 3
< 0.1%
96141 3
< 0.1%
96140 4
0.1%
96137 4
0.1%
96136 4
0.1%

Interactions

2024-10-18T19:37:05.481173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:34.826447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:37.209869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:39.240854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:41.291151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:43.710740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:45.749178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:47.738387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:50.160147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:52.300166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:54.437018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:56.677231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:59.356483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:37:01.432266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:37:03.493208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2024-10-18T19:36:39.103602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:41.149113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:43.569364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:45.606880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:47.601727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:50.017219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:52.135838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:54.289157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:56.523479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:36:59.211220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:37:01.288937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:37:03.354404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-10-18T19:37:05.342452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-10-18T19:37:19.303976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeAvg_Monthly_GB_DownloadAvg_Monthly_Long_Distance_ChargesCLTVChurn_CategoryChurn_LabelChurn_ReasonChurn_ScoreContractDevice_Protection_PlanGenderInternet_ServiceInternet_TypeMarriedMonthly_ChargeMultiple_LinesNumber_of_DependentsNumber_of_ReferralsOfferOnline_BackupOnline_SecurityPaperless_BillingPayment_MethodPhone_ServicePremium_Tech_SupportSatisfaction_ScoreSenior_CitizenStreaming_MoviesStreaming_MusicStreaming_TVTenure_in_MonthsTotal_ChargesTotal_Extra_Data_ChargesTotal_Long_Distance_ChargesTotal_RefundsTotal_RevenueUnder_30Unlimited_DataZip_Code
Age1.000-0.239-0.0120.0000.0660.1450.0550.0810.0270.0450.0000.1720.0660.0290.1350.139-0.120-0.0160.0360.0550.0430.1410.0990.0230.0480.0600.9280.1080.1800.0840.0120.0670.0310.0100.0200.0520.9240.128-0.007
Avg_Monthly_GB_Download-0.2391.000-0.0490.0230.0710.1480.0610.0630.0940.2930.0000.7390.2180.0710.4980.1610.0290.0370.0180.2970.2800.2230.1290.1270.2740.0960.2420.3100.3450.3030.0530.2950.132-0.0260.0170.2170.6940.555-0.018
Avg_Monthly_Long_Distance_Charges-0.012-0.0491.0000.0240.0170.0100.0040.0210.0260.0430.0320.1110.2200.0000.1410.207-0.0060.0080.0000.0350.0570.0000.0230.7180.0720.0160.0230.0000.0030.0140.0140.059-0.0090.651-0.0130.2090.0440.0820.006
CLTV0.0000.0230.0241.0000.0660.1410.057-0.0790.2060.1390.0370.0000.0140.1650.1080.1460.0560.1310.1450.1510.1550.0150.0390.0250.1380.0660.0000.1240.1100.1090.3670.3100.0250.2400.0090.3240.0130.000-0.002
Churn_Category0.0660.0710.0170.0661.0001.0000.9930.3320.3200.0730.0000.2370.0770.1500.1260.0450.1090.1390.1180.0850.1720.1930.1560.0080.1640.4460.1510.0560.0450.0650.1620.0930.0230.1080.0000.1080.0500.1720.077
Churn_Label0.1450.1480.0100.1411.0001.0000.9990.7440.4530.0650.0000.2270.1020.1500.2760.0380.2470.3170.2600.0810.1700.1910.2180.0000.1640.8590.1500.0600.0440.0620.3640.2140.0230.2460.0290.2440.0530.1660.062
Churn_Reason0.0550.0610.0040.0570.9930.9991.0000.2470.3180.0710.0000.2510.0760.1500.0990.0670.0760.0960.1170.0950.1770.1910.1590.0270.1630.4800.1680.0660.0550.0830.1200.0680.0000.0750.0000.0780.0440.1810.098
Churn_Score0.0810.0630.021-0.0790.3320.7440.2471.0000.2380.0560.0000.1600.0640.1160.1300.040-0.180-0.1680.0870.0560.1260.1410.1050.0370.1310.3240.1160.0360.0060.034-0.240-0.1460.031-0.140-0.019-0.1650.0410.113-0.021
Contract0.0270.0940.0260.2060.3200.4530.3180.2381.0000.2270.0000.2020.0280.2810.2280.1210.1250.2210.3390.1690.2360.1500.1170.0000.2730.2850.0290.1250.0880.1160.4970.3460.0410.3210.0390.3600.0000.1400.024
Device_Protection_Plan0.0450.2930.0430.1390.0730.0650.0710.0560.2271.0000.0000.3800.1460.1530.5190.2000.0090.1270.2460.3030.2750.1030.0790.0700.3330.0980.0580.4020.3490.3900.3590.5220.0760.2100.0000.4710.0000.2960.000
Gender0.0000.0000.0320.0370.0000.0000.0000.0000.0000.0001.0000.0000.0070.0000.0070.0000.0130.0000.0200.0060.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.014
Internet_Service0.1720.7390.1110.0000.2370.2270.2510.1600.2020.3800.0001.0000.3880.0000.9670.2100.1720.0420.0460.3810.3330.3200.2730.1710.3360.2690.1820.4180.3880.4150.0210.4280.1550.0450.0060.3100.0330.7550.043
Internet_Type0.0660.2180.2200.0140.0770.1020.0760.0640.0280.1460.0070.3881.0000.0000.5630.1910.0000.0280.0000.1550.3130.0560.0490.4440.3060.0810.0960.0280.0790.0230.0000.1520.0440.1090.0000.0920.0310.2890.000
Married0.0290.0710.0000.1650.1500.1500.1500.1160.2810.1530.0000.0000.0001.0000.1560.1410.3620.6820.2560.1410.1420.0080.0620.0120.1190.1210.0110.1170.0880.1240.3780.3240.0000.2690.0310.3330.0090.0140.027
Monthly_Charge0.1350.4980.1410.1080.1260.2760.0990.1300.2280.5190.0070.9670.5630.1561.0000.526-0.1330.0800.1250.4880.4250.3600.2210.6640.4450.1500.2340.6650.5530.6680.2760.6380.1230.3170.0370.5690.0600.729-0.012
Multiple_Lines0.1390.1610.2070.1460.0450.0380.0670.0400.1210.2000.0000.2100.1910.1410.5261.0000.0120.0750.2370.2020.0970.1630.1520.2790.1000.0550.1420.2580.1930.2570.3330.4690.0610.3330.0460.4610.0330.1590.031
Number_of_Dependents-0.1200.029-0.0060.0560.1090.2470.076-0.1800.1250.0090.0130.1720.0000.362-0.1330.0121.0000.3560.0420.0000.0450.1210.0680.0290.0210.1010.1730.0700.0310.0540.1350.042-0.0310.0910.0180.0650.0430.1280.017
Number_of_Referrals-0.0160.0370.0080.1310.1390.3170.096-0.1680.2210.1270.0000.0420.0280.6820.0800.0750.3561.0000.1060.1130.1460.0530.0540.0000.1200.1300.0200.0560.0530.0710.3830.327-0.0250.2570.0370.3390.0080.0000.006
Offer0.0360.0180.0000.1450.1180.2600.1170.0870.3390.2460.0200.0460.0000.2560.1250.2370.0420.1061.0000.2500.2340.0000.0680.0100.2240.1080.0650.1910.1610.1910.5620.3470.0290.2580.0160.3480.0000.0320.051
Online_Backup0.0550.2970.0350.1510.0850.0810.0950.0560.1690.3030.0060.3810.1550.1410.4880.2020.0000.1130.2501.0000.2830.1260.0950.0500.2940.1100.0650.2740.2450.2820.3580.5090.1000.2430.0000.4720.0000.2830.022
Online_Security0.0430.2800.0570.1550.1720.1700.1770.1260.2360.2750.0120.3330.3130.1420.4250.0970.0450.1460.2340.2831.0000.0000.0440.0920.3540.3210.0360.1870.1950.1750.3260.4200.0570.1980.0310.3830.0290.2640.012
Paperless_Billing0.1410.2230.0000.0150.1930.1910.1910.1410.1500.1030.0000.3200.0560.0080.3600.1630.1210.0530.0000.1260.0001.0000.1850.0110.0360.1650.1560.2110.1660.2230.0000.1580.0430.0190.0000.1330.0370.2450.004
Payment_Method0.0990.1290.0230.0390.1560.2180.1590.1050.1170.0790.0000.2730.0490.0620.2210.1520.0680.0540.0680.0950.0440.1851.0000.0240.0490.1350.1490.1780.1340.1810.0950.1160.0320.0720.0140.1020.0420.1970.049
Phone_Service0.0230.1270.7180.0250.0080.0000.0270.0370.0000.0700.0000.1710.4440.0120.6640.2790.0290.0000.0100.0500.0920.0110.0241.0000.0950.0410.0000.0300.0370.0190.0000.1510.0350.3410.0000.1850.0000.1210.027
Premium_Tech_Support0.0480.2740.0720.1380.1640.1640.1630.1310.2730.3330.0000.3360.3060.1190.4450.1000.0210.1200.2240.2940.3540.0360.0490.0951.0000.1730.0590.2790.2760.2780.3250.4370.0950.1880.0300.3940.0170.2510.010
Satisfaction_Score0.0600.0960.0160.0660.4460.8590.4800.3240.2850.0980.0000.2690.0810.1210.1500.0550.1010.1300.1080.1100.3210.1650.1350.0410.1731.0000.1250.0910.0810.0890.1570.1050.0000.1040.0000.1140.0540.2150.014
Senior_Citizen0.9280.2420.0230.0000.1510.1500.1680.1160.0290.0580.0000.1820.0960.0110.2340.1420.1730.0200.0650.0650.0360.1560.1490.0000.0590.1251.0000.1190.1470.1040.0230.1140.0570.0220.0120.0850.2180.1390.000
Streaming_Movies0.1080.3100.0000.1240.0560.0600.0660.0360.1250.4020.0000.4180.0280.1170.6650.2580.0700.0560.1910.2740.1870.2110.1780.0300.2790.0910.1191.0000.8480.5330.2830.5180.0940.1870.0000.4630.0000.3180.000
Streaming_Music0.1800.3450.0030.1100.0450.0440.0550.0060.0880.3490.0000.3880.0790.0880.5530.1930.0310.0530.1610.2450.1950.1660.1340.0370.2760.0810.1470.8481.0000.4550.2340.4400.0770.1490.0000.3920.1230.2960.014
Streaming_TV0.0840.3030.0140.1090.0650.0620.0830.0340.1160.3900.0000.4150.0230.1240.6680.2570.0540.0710.1910.2820.1750.2230.1810.0190.2780.0890.1040.5330.4551.0000.2770.5120.0710.1840.0000.4590.0110.3230.000
Tenure_in_Months0.0120.0530.0140.3670.1620.3640.120-0.2400.4970.3590.0000.0210.0000.3780.2760.3330.1350.3830.5620.3580.3260.0000.0950.0000.3250.1570.0230.2830.2340.2771.0000.8890.0190.6630.0840.9130.0000.0060.010
Total_Charges0.0670.2950.0590.3100.0930.2140.068-0.1460.3460.5220.0000.4280.1520.3240.6380.4690.0420.3270.3470.5090.4200.1580.1160.1510.4370.1050.1140.5180.4400.5120.8891.0000.0780.6500.0870.9780.0140.3280.003
Total_Extra_Data_Charges0.0310.132-0.0090.0250.0230.0230.0000.0310.0410.0760.0000.1550.0440.0000.1230.061-0.031-0.0250.0290.1000.0570.0430.0320.0350.0950.0000.0570.0940.0770.0710.0190.0781.000-0.0040.0090.0670.0170.433-0.002
Total_Long_Distance_Charges0.010-0.0260.6510.2400.1080.2460.075-0.1400.3210.2100.0270.0450.1090.2690.3170.3330.0910.2570.2580.2430.1980.0190.0720.3410.1880.1040.0220.1870.1490.1840.6630.650-0.0041.0000.0610.7780.0110.0290.010
Total_Refunds0.0200.017-0.0130.0090.0000.0290.000-0.0190.0390.0000.0000.0060.0000.0310.0370.0460.0180.0370.0160.0000.0310.0000.0140.0000.0300.0000.0120.0000.0000.0000.0840.0870.0090.0611.0000.0820.0000.024-0.004
Total_Revenue0.0520.2170.2090.3240.1080.2440.078-0.1650.3600.4710.0000.3100.0920.3330.5690.4610.0650.3390.3480.4720.3830.1330.1020.1850.3940.1140.0850.4630.3920.4590.9130.9780.0670.7780.0821.0000.0000.2350.007
Under_300.9240.6940.0440.0130.0500.0530.0440.0410.0000.0000.0000.0330.0310.0090.0600.0330.0430.0080.0000.0000.0290.0370.0420.0000.0170.0540.2180.0000.1230.0110.0000.0140.0170.0110.0000.0001.0000.0290.000
Unlimited_Data0.1280.5550.0820.0000.1720.1660.1810.1130.1400.2960.0000.7550.2890.0140.7290.1590.1280.0000.0320.2830.2640.2450.1970.1210.2510.2150.1390.3180.2960.3230.0060.3280.4330.0290.0240.2350.0291.0000.021
Zip_Code-0.007-0.0180.006-0.0020.0770.0620.098-0.0210.0240.0000.0140.0430.0000.027-0.0120.0310.0170.0060.0510.0220.0120.0040.0490.0270.0100.0140.0000.0000.0140.0000.0100.003-0.0020.010-0.0040.0070.0000.0211.000

Missing values

2024-10-18T19:37:07.726871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-18T19:37:08.432374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Satisfaction_ScoreChurn_LabelChurn_ScoreCLTVChurn_CategoryChurn_ReasonNumber_of_ReferralsTenure_in_MonthsOfferPhone_ServiceAvg_Monthly_Long_Distance_ChargesMultiple_LinesInternet_ServiceInternet_TypeAvg_Monthly_GB_DownloadOnline_SecurityOnline_BackupDevice_Protection_PlanPremium_Tech_SupportStreaming_TVStreaming_MoviesStreaming_MusicUnlimited_DataContractPaperless_BillingPayment_MethodMonthly_ChargeTotal_ChargesTotal_RefundsTotal_Extra_Data_ChargesTotal_Long_Distance_ChargesTotal_RevenueGenderAgeUnder_30Senior_CitizenMarriedNumber_of_DependentsCityZip_Code
03No652205.0Not ChurnedNot Churned29No OfferTrue42.39FalseTrueCable16.0FalseTrueFalseTrueTrueFalseFalseTrueOne YearTrueCredit Card65.60593.300.000.0381.51974.81Female37FalseFalseTrue0Frazier Park93225
15No665414.0Not ChurnedNot Churned09No OfferTrue10.69TrueTrueCable10.0FalseFalseFalseFalseFalseTrueTrueFalseMonth-to-MonthFalseCredit Card59.90542.4038.3310.096.21610.28Male46FalseFalseFalse0Glendale91206
21Yes714479.0CompetitorCompetitor had better devices04Offer ETrue33.65FalseTrueFiber Optic30.0FalseFalseTrueFalseFalseFalseFalseTrueMonth-to-MonthTrueBank Withdrawal73.90280.850.000.0134.60415.45Male50FalseFalseFalse0Costa Mesa92627
31Yes913714.0DissatisfactionProduct dissatisfaction113Offer DTrue27.82FalseTrueFiber Optic4.0FalseTrueTrueFalseTrueTrueFalseTrueMonth-to-MonthTrueBank Withdrawal98.001237.850.000.0361.661599.51Male78FalseTrueTrue0Martinez94553
41Yes683464.0DissatisfactionNetwork reliability33No OfferTrue7.38FalseTrueFiber Optic11.0FalseFalseFalseTrueTrueFalseFalseTrueMonth-to-MonthTrueCredit Card83.90267.400.000.022.14289.54Female75FalseTrueTrue0Camarillo93010
53No555108.0Not ChurnedNot Churned09Offer ETrue16.77FalseTrueCable73.0FalseFalseFalseTrueTrueTrueTrueTrueMonth-to-MonthTrueCredit Card69.40571.450.000.0150.93722.38Female23TrueFalseFalse3Midpines95345
63No265011.0Not ChurnedNot Churned171Offer ATrue9.96FalseTrueFiber Optic14.0TrueTrueTrueTrueTrueTrueTrueTrueTwo YearTrueBank Withdrawal109.707904.250.000.0707.168611.41Female67FalseTrueTrue0Lompoc93437
74No494604.0Not ChurnedNot Churned863Offer BTrue12.96TrueTrueFiber Optic7.0TrueFalseFalseTrueFalseFalseFalseFalseTwo YearTrueCredit Card84.655377.800.0020.0816.486214.28Male52FalseFalseTrue0Napa94558
83No345525.0Not ChurnedNot Churned07Offer ETrue10.53FalseTrueDSL21.0TrueFalseFalseFalseFalseFalseFalseTrueTwo YearTrueBank Withdrawal48.20340.350.000.073.71414.06Female68FalseTrueFalse0Simi Valley93063
93No255509.0Not ChurnedNot Churned365No OfferTrue28.46TrueTrueCable14.0TrueTrueTrueTrueTrueTrueTrueTrueTwo YearTrueCredit Card90.455957.900.000.01849.907807.80Female43FalseFalseTrue1Sheridan95681
Satisfaction_ScoreChurn_LabelChurn_ScoreCLTVChurn_CategoryChurn_ReasonNumber_of_ReferralsTenure_in_MonthsOfferPhone_ServiceAvg_Monthly_Long_Distance_ChargesMultiple_LinesInternet_ServiceInternet_TypeAvg_Monthly_GB_DownloadOnline_SecurityOnline_BackupDevice_Protection_PlanPremium_Tech_SupportStreaming_TVStreaming_MoviesStreaming_MusicUnlimited_DataContractPaperless_BillingPayment_MethodMonthly_ChargeTotal_ChargesTotal_RefundsTotal_Extra_Data_ChargesTotal_Long_Distance_ChargesTotal_RevenueGenderAgeUnder_30Senior_CitizenMarriedNumber_of_DependentsCityZip_Code
70334No694480.0Not ChurnedNot Churned01Offer ETrue49.51FalseFalseFiber Optic0.0FalseFalseFalseFalseFalseFalseFalseFalseMonth-to-MonthFalseCredit Card18.9018.900.00.049.5168.41Male24TrueFalseFalse0Sierraville96126
70343No683384.0Not ChurnedNot Churned147No OfferTrue42.29FalseTrueFiber Optic22.0FalseTrueFalseFalseTrueFalseFalseFalseOne YearTrueBank Withdrawal84.954018.050.080.01987.636085.68Male72FalseTrueTrue1Bakersfield93301
70353No385545.0Not ChurnedNot Churned07Offer ETrue36.49FalseTrueFiber Optic42.0FalseTrueFalseFalseTrueTrueTrueTrueOne YearTrueCredit Card94.05633.450.00.0255.43888.88Female20TrueFalseFalse0Los Angeles90022
70361Yes905773.0CompetitorCompetitor had better devices01Offer ETrue42.09FalseTrueFiber Optic9.0FalseFalseFalseFalseFalseFalseFalseTrueMonth-to-MonthTrueCredit Card70.1570.150.00.042.09112.24Female53FalseFalseFalse0Hume93628
70371Yes855822.0CompetitorCompetitor made better offer04No OfferTrue2.01FalseFalseFiber Optic0.0FalseFalseFalseFalseFalseFalseFalseFalseMonth-to-MonthFalseBank Withdrawal20.9585.500.00.08.0493.54Female36FalseFalseFalse0Fallbrook92028
70384No593161.0Not ChurnedNot Churned013Offer DTrue46.68FalseTrueDSL59.0TrueFalseFalseTrueFalseFalseTrueTrueOne YearFalseCredit Card55.15742.900.00.0606.841349.74Female20TrueFalseFalse0La Mesa91941
70391Yes685248.0DissatisfactionProduct dissatisfaction122Offer DTrue16.20TrueTrueFiber Optic17.0FalseFalseFalseFalseFalseTrueTrueTrueMonth-to-MonthTrueBank Withdrawal85.101873.700.00.0356.402230.10Male40FalseFalseTrue0Riverbank95367
70405No335870.0Not ChurnedNot Churned02Offer ETrue18.62FalseTrueDSL51.0FalseTrueFalseFalseFalseFalseFalseTrueMonth-to-MonthTrueCredit Card50.3092.750.00.037.24129.99Male22TrueFalseFalse0Elk95432
70413No594792.0Not ChurnedNot Churned567Offer ATrue2.12FalseTrueCable58.0TrueFalseTrueTrueFalseTrueTrueTrueTwo YearFalseCredit Card67.854627.650.00.0142.044769.69Male21TrueFalseTrue0Solana Beach92075
70423No205639.0Not ChurnedNot Churned163No OfferFalse0.00FalseTrueCable5.0TrueTrueTrueFalseTrueTrueTrueTrueTwo YearFalseBank Withdrawal59.003707.600.00.00.003707.60Male36FalseFalseTrue0Sierra City96125